Deep learning for IoT big data and streaming analytics: A survey
In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …
and/or generate various sensory data over time for a wide range of fields and applications …
Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions
The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …
devices is leading to the critical challenge of fulfilling diverse communication requirements …
Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices
A recent trend in deep neural network (DNN) development is to extend the reach of deep
learning applications to platforms that are more resource and energy-constrained, eg …
learning applications to platforms that are more resource and energy-constrained, eg …
A configurable cloud-scale DNN processor for real-time AI
J Fowers, K Ovtcharov, M Papamichael… - 2018 ACM/IEEE 45th …, 2018 - ieeexplore.ieee.org
Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
YodaNN: An architecture for ultralow power binary-weight CNN acceleration
Convolutional neural networks (CNNs) have revolutionized the world of computer vision
over the last few years, pushing image classification beyond human accuracy. The …
over the last few years, pushing image classification beyond human accuracy. The …
A high energy efficient reconfigurable hybrid neural network processor for deep learning applications
Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to
NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated …
NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated …
YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights
Convolutional Neural Networks (CNNs) have revolutionized the world of image classification
over the last few years, pushing the computer vision close beyond human accuracy. The …
over the last few years, pushing the computer vision close beyond human accuracy. The …
Scaling up silicon photonic-based accelerators: Challenges and opportunities
Digital accelerators in the latest generation of complementary metal–oxide–semiconductor
processes support, multiply, and accumulate (MAC) operations at energy efficiencies …
processes support, multiply, and accumulate (MAC) operations at energy efficiencies …
Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices
M Verhelst, B Moons - IEEE Solid-State Circuits Magazine, 2017 - ieeexplore.ieee.org
Deep learning has recently become immensely popular for image recognition, as well as for
other recognition and pattern matching tasks in, eg, speech processing, natural language …
other recognition and pattern matching tasks in, eg, speech processing, natural language …
An energy-efficient precision-scalable ConvNet processor in 40-nm CMOS
B Moons, M Verhelst - IEEE Journal of solid-state Circuits, 2016 - ieeexplore.ieee.org
A precision-scalable processor for low-power ConvNets or convolutional neural networks is
implemented in a 40-nm CMOS technology. To minimize energy consumption while …
implemented in a 40-nm CMOS technology. To minimize energy consumption while …